Feb 16, 2016 · Abstract:We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying ...
We show that gradient descent converges to a local minimizer, almost surely with random initial- ization. This is proved by applying the Stable Manifold ...
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Feb 17, 2016 · We show that gradient descent converges to a local minimizer, almost surely with random initializa- tion. This is proved by applying the Stable ...
First-order descent methods can indeed escape strict saddle points when assisted by near isotropic noise. [21] establishes convergence of the Robbins-Monro ...
We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold Theorem ...
Nov 26, 2019 · If you initialize gradient descent with a point x0 which is a minimizer of the objective function but not a least norm minimizer, then the ...
Mar 26, 2016 · They are characterized by the eigenvalues of the Hessian at those critical points. If all eigenvalues are non-zero and either strictly positive ...
Dec 6, 2019 · Ordinary least squares is minimizing the sum of squared errors for a linear function predicting your test data. You can use the closed form ...
We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold ...
Jan 19, 2021 · I know for sure that gradient descent, i.e., the update equation. xk+1=xk−ϵk∇f(xk). converge to the unique minimizer of f with domain ...